Summary of Skill-enhanced Reinforcement Learning Acceleration From Demonstrations, by Hanping Zhang et al.
Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations
by Hanping Zhang, Yuhong Guo
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Skill-enhanced Reinforcement Learning Acceleration (SeRLA) method aims to improve Reinforcement Learning (RL) by leveraging expert demonstrations. It uses a skill-level adversarial Positive-Unlabeled (PU) learning model to extract useful prior knowledge from limited expert data and general low-cost demonstration data, which is then used to train a skill policy network. The method also includes a simple data enhancement technique to alleviate data sparsity. Experimental results show that SeRLA achieves state-of-the-art performance in accelerating RL on downstream tasks, particularly in the early learning phase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SeRLA is a new way to help machines learn faster by using expert demonstrations. It takes limited expert data and combines it with more general data to teach the machine what skills are important. Then, it uses this knowledge to train another part of the machine that makes decisions. This helps the machine learn even better over time. The results show that SeRLA is very good at speeding up the learning process, especially when the machine is just starting out. |
Keywords
» Artificial intelligence » Reinforcement learning
